Artificial Intelligence and Decision Support Applications in Liver Hydatid Disease: Detection, Classification, and Complication Prediction

Basit Öğe Kaydı
dc.contributor.authorKaraduman, Mucahit
dc.contributor.authorYildirim, Muhammed
dc.contributor.authorAkbulut, Sami
dc.date.accessioned2026-06-19T06:31:54Z
dc.date.available2026-06-19T06:31:54Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractHydatid disease, caused by Echinococcus spp., is a parasitic infection commonly observed in endemic regions such as the Middle East, South America, and Central Asia. Imaging modalities like ultrasonography, computed tomography, and magnetic resonance imaging play a crucial role in diagnosing and managing this disease. However, these techniques have some limitations, particularly concerning diagnostic accuracy, operator dependency, and the inability to predict complications in advance. This chapter comprehensively addresses the use of artificial intelligence (AI) and clinical decision support systems in managing liver hydatid disease within this context. Since the disease most commonly affects the liver, the chapter specifically focuses on liver hydatid disease. AI-based technologies are increasingly utilized to overcome these challenges and optimize diagnostic processes. Deep learning algorithms (e.g., CNN, U-Net) have demonstrated high accuracy in analyzing imaging data. These algorithms enable the automated staging of liver hydatid cysts and predict the risk of complications. Notably, the automation of staging systems accelerates clinical decision-making and reduces discrepancies among expert opinions. Furthermore, surgical clinical decision support systems and complication prediction models not only enhance diagnostic processes but also make treatment planning more reliable. Despite the promising potential of AI models, their widespread clinical adoption faces obstacles such as the lack of high-quality data sets and the challenge of making model decisions interpretable. Therefore, multicenter studies and model validations based on extensive data sets are essential for integrating AI more effectively into clinical practice. In conclusion, AI-powered clinical decision support systems hold significant potential for standardizing and expediting the diagnostic and therapeutic processes in liver hydatid disease. However, further research is necessary to ensure their seamless integration into clinical practice. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.doi10.1007/978-3-031-97277-5_13
dc.identifier.endpage175
dc.identifier.isbn978-303197277-5
dc.identifier.isbn978-303197276-8
dc.identifier.scopus2-s2.0-105023600483
dc.identifier.scopusqualityN/A
dc.identifier.startpage161
dc.identifier.urihttps://doi.org/10.1007/978-3-031-97277-5_13
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4831
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science+Business Media
dc.relation.ispartofHydatid Disease: Diagnosis, Treatment and Follow Up Strategies
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260612
dc.subjectArtificial Intelligence
dc.subjectClinical Decision Support Systems
dc.subjectComplication Prediction
dc.subjectDeep Learning
dc.subjectHydatid Disease
dc.subjectImaging Analysis
dc.titleArtificial Intelligence and Decision Support Applications in Liver Hydatid Disease: Detection, Classification, and Complication Prediction
dc.typeBook Chapter

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu